With the Web 2.0 movement, more people are supplying content, and more means are being established to share content. People are trying to provide content in ways others will find useful. More content is being digitalized so it can be shared electronically. As search systems increase in size and complexity, queries tend to generate more results than the typical information seeker can effectively go through to find the best or desired results. Typical returned result sets for queries contain “noise”, results that are not deemed relevant, significant, and/or content-laden by the end-user. There is a need to eliminate noise so end-users can effectively and efficiently go through search results. Furthermore, as electronic data transfer and communication become more complex, there are more items that can be searched using computer-implemented methods. The intent of an end-user's search may include finding items that are located in a variety of places (e.g., user's desktop, client, document, on database located on another server, on the Internet, etc.) and can be in many types (e.g., text, images, file directories, audio, video, web pages, email addresses, IM accounts, files, etc.). The application of many search systems can also produce more noise, and the process may require more time and energy for the end-user to conduct search. Avenues to reduce noise, especially in integrated search systems, included the introduction of better “intent-driven” search, the application of social awareness in search systems, and the utilization of intelligence systems, including contextual and collective intelligence.
Many “intent-driven” approaches to optimizing search results focus on refining queries to hone in on the content searchers want. “End-user intent” is defined herein as the goal or purpose of end-user in submitting a search query or plurality of queries to a result provider in attempt to obtain results to help said end-user meet said goal or purpose. An example of such techniques is providing a query session that utilizes additional prompts before and/or after an initial query to narrow result sets returned. These approaches have generally been applied, for example, to web searches focused on one product, such as homes, jobs, or personal vehicles. Users may select search (query) criteria from pre-selected categories. To further optimize search, some systems allow pseudo “persistent” searches, commonly referred to as “saved searches”. A “saved” search generally only allows users to save search (query) criteria without having to re-enter the criteria in the prompts. Such searches generally do not allow for further refinement of items though interactions, such as “delete”. “Saved” searches require users to reissue the same query and, therefore are not truly “persistent”. “Persistent” searches are similar to “channels” or RSS streams, discussed herein. While current “saved” search is most suitable for systems with only one goal, such as finding property or a vehicle, it is not practical in situations where the relationship of items searched to the intent of query issued is only easily known by the end-user issuing the queries. For example, in a search for a “wedding gift”, multiple, independent searches may be performed that through keywords in the queries seem unrelated, but, through the intent of the end-user, are more apparent. Saving the queries alone is not as helpful to the end-user who might be evaluating a wide range of items whose relevance is only truly deciphered by the end-user.
Current search systems are limited to query-based sessions, which do not allow the incorporation of influence of multiple, seemingly disparate queries aimed at the same purpose or project. In other words, current search systems fail to capture user intent outside of queries issued. While actual keywords in queries are vital in knowing the subject matter of the search, they do not necessarily capture the intent or larger goals of the queries. Seemingly different queries (e.g., different keywords) are not able to work in conjunction to optimize and refine search.
When queries do pivot around keywords or other item attributes, such as time posted or source's geographical location, then query sessions are practical. However, current systems and methods are usually based on a “natural progression” of refining queries themselves to extract a smaller result set that better fits the intention of the end-user. These methods begin with an initial query and build a result set from there. These methods are tied to the result provider itself and ultimately can only produce one result set to the string of queries. Structured query refining methods try to imitate the flow of logic of a generic, “all-encompassing” end-user. However, these often rigid, pre-structured query sessions limit the freedom of the end-user to link queries by how she intends the queries to be associated. There is a need for more flexible search refining methods to provide an end-user the ability to associate and structure multiple queries based on her particular intent.
One query session approach is employed by Aware Search by Stottler Henke Associates, Inc., of San Mateo, Calif. The Aware Search client allows users to rate an item as “good” or “bad” and weigh key terms. This enables the system to refine results for the end-user even after the initial query is sent. Users are able to organize searches (queries) into folders; however, each search is independently understood by Aware. While projects are organized in familiar folders, “awareness” is not shared among a project, i.e., grouped searches (queries). Results may again appear in another query within the same project the end-user has already rated “bad”. Interactions cannot be combined within a whole project to reflect the end-user's overall intent for the grouped queries. In other words, while queries can be organized around the end-user's categorization of each query, evaluations on individual results cannot cross queries and be used within the same project.
Organizing search results based on query alone is insufficient to ensure that a particular result meets the intention of said end-user. For example, a query of “Martin Luther”, the German monk, could return results related to “Martin Luther King”, an American Civil Rights leader. Currently, one can refine said search for that particular query. However, if the end-user issues another query that shares no similar keywords with the first query, “Martin Luther”, such as “Protestant Reformation”, the query must currently be independently refined. The end-user cannot apply the interactions to particular results in the first query to results in the second query. Furthermore, if a third query is issued without any similar keywords, such as “Catholic Church”, the end-user is not able to link this query with either of the previous two. The end-user has a specific application for the information in which the search is intended. Storing information based on query alone will not capture end-user's intent.
Productivity is also to be gained by dividing search among a plurality of searchers utilizing the same information system. For example, searchers within the same organization, but different working groups, may access the same databases and share the same files, but apply those files to specific, independent projects. There is currently need to optimize searches for specific use without destroying the integrity of the information for others' use. For example, a marketing firm may have a database of photos to be utilized by its employees for ad campaigns. Currently, employees searching for files might save specific files they require in their own project folders. In their search, they cannot delete files they are not interested in using without deleting the actual file in the database. Deleting such files could present problems to other users who might require access to those same files.
Productivity is also gained through combined search efforts of multiple searchers. Many individual end-users assess items stored in information systems, and there is much to be gained by tapping into their evaluations of items stored in these search systems. Aware Search provides limited search collaboration through sharing search data through the exporting and importing of data files. Through this method only one user is able to access the same exact search at a time and perform interactions. (Two people utilizing similar data files have different searches once they make changes to the files.) For bi-directional search collaboration, first a collaborating user must import the search data to gain the same results. Aware Search does not control what interactions the new user can then perform on that data, neglecting possible hierarchies in the collaborating relationship. Then, another file is sent back to the initial user, who must now establish version control and organize these search data files. Furthermore, Aware Search does not facilitate near real-time sharing of data nor is the system “socially aware”.
Current cataloging, listing, and other similar systems have attempted to include social context by introducing systems that allows end-users to rate items, add commentary to items (e.g., “opinions”), “tag” (categorize) items found in a search, among others. An example is Amazon.com, based in Seattle, Wash., which offers a site with these common approaches to provide social context. The problem with such common methods of social context is social relationships are “inferred” based on the common relationship to a particular item in the search rather than the social contexts in which the end-user truly finds herself situated. In other words, every end-user who views an item becomes part of the “aggregate” or “group”, and commonality of end-users is assumed based on similar query or interest in an item. The interactions, such as tagging and rating, are assumed “valuable” because of the assumed commonalty. These systems do not take into account commonalities the end-user values or the degree of trust the end-user has or established with other particular users. For query systems that cater to many end-users, a group of three hundred commenting on an item can become overwhelming for a particular end-user and not particularly useful. For example, a system that allows users to rate the “value” of a comment or item does not necessarily cater to the whole population of users in a given community. A comment that is rated high by one user may have been low to another. Basing “value” using the whole population of any community requires that the “norm” or “mean” of the “aggregate” wins out in the battle of competing voices. A large minority voice may be shut out, or a small, but vocal minority voice may be overvalued in these systems. Current systems do not necessarily capture the needs of some individual end-users who do not fall in the “norm”. An example of this is the system for evaluating comments for “lens” employed by SQUIDOO LLC of Irvington, N.Y. “Lens” used by SQUIDOO are not related to search but rather is “content” driven. A “lens” is “one person's look at something online”. Group collaboration is accomplished by people selecting a lens related to a topic they are interested in and providing content regarding said topic. “Links Plexo” provides a plurality of users the ability to add links related to a lens, and a “lens” allow users to comment on topics. A group lens additionally provides people the ability to swarm around content people are providing to the group; however, they do not provide a search system that integrates the established “lens”. In other words, although a search system might search content within “lens” or titles of said “lens”, the search is not based on interactions of results associated with user or group identities tied to a “lens”. SQUIDOO's “lens” and “group lens” are not “search driven”. There is a need to have systems and methods that can be both search and content driven so amass content can be more effectively shared.
Unlike SQUIDOO lens, “wiki” as used in Wikipedia by Wikimedia Foundation, Inc. of St. Petersburg, Fla., is too centralized. People compete to add content on a particular topic. Data provided is assumed to be “unbiased”, and the electronic encyclopedia is meant to serve as an authoritative voice using multiple voices. The problem with this approach is many voices are left out or edited out. Wiki “wars” can happen as people try to establish their content over another. There needs to be a balance where multiple people can provide content; however, the content is not overwhelming to the end-user.
“Social awareness” in reference to search is defined herein as direct influence of a plurality of users to affect results displayed in a search. Social search can be either “explicit” or “implicit”. “Explicit” search is where social relationships are “explicitly” shared to guide search. In “explicit” social search, end-users may know and have an established relationship with other end-users.
An example of explicit search methods is XFN utilized by Technorati.com of San Francisco, Calif. Relationships are used to share information to particular users. XFN uses keywords in HTML to represent relationships of a node to map true human relationships from those nodal relationships. People can be “friends”, “acquaintances”, or other types of relationships so the character of each relationship is known. This decentralized approach aides any engine to find these relationships. This approach is not practical for closed or private forums where search holds sensitive information. Also, this model places priority on the content providers' nodal relationships rather than the searcher's social relationships. The searcher for any given query might place more or less value on known or unknown relationships. For example, in one forum where the end-user is an expert, she may only want items related to those within her network. However, in areas she is not sure the expertise of her network, she might prefer exploring items from users unknown to her.
“Implicit” search is when social relationships are implied because end-users share something in common; however, end-users do not necessarily personally know other end-users in which data are shared. Implicit engines use implied relationships as a filter. These systems use the content of items search to imply the links and relationships of social networks. Those networks are then used as a filter to provide presumably better results on what an end-user might possibly want. Current systems use implicit social search to provide “collective intelligence”. “Collective intelligence” in reference to search is defined herein as application and/or summation of data gathered from a plurality of end-users. Current collective intelligence models focus on the content of the items and not the actions and true preferences of the end-users. By using algorithms placed on the content rather than the user, these systems are only valuable when such relationships can be inferred by content and cannot be used in unrelated queries made by similar social classes. For example, an end-user who values parental opinion might query a movie unrelated to any keyword in the content associated with a parental role. The filter may be of value; however, this approach cannot use social filters for every query and cannot be applied to all content.
“Implicit” social search is more common, and includes most search systems that utilize aggregated information from other users to help guide the search of a particular end-user. Public tagging and social bookmarking systems (such as utilized by Yahoo.com of Sunnyvale, Calif., under del.icio.us, and Digg.com of San Francisco, Calif., among others) primarily rely on implicit social search and are currently popular ways to include collective intelligence within search. The social bookmarking site, Digg.com, primarily utilizes implicit, as well as “egalitarian” search to summarize news feeds. Egalitarian search systems treat all end-users with equal status and weight in relation to other end-users in the system. Digg.com's social bookmarking displays items according to the number of “digg”s it received. All users are able to “digg” an item or “bury” an item, and have equal opportunity to influence results. However, “collective intelligence” is dependent on the relationship of the “collective” to the end-user and how apparent biases that occur from the “collective” are to the end-user. Some systems use algorithms to try to infer the importance or value of an item to a particular end-user prior to the end-user making any valuations or actions. Biases within these “intelligent” systems are not apparent to the end-user. Digg.com allows user to include “buried” stories (those identified by other users as items to “bury”); however, it is not obvious to the end-user the biases of why an item was buried.
Systems that view each member as equal or establish a universal rank among all members do not capture effectively the relationships each member of the group has in relation to said group or community. For example, this type of egalitarian search system cannot be applied within an elementary school community consisting of young students, teachers, and administrators where members have different responsibilities, discernment, interest, etc. Administrators and teachers may want to possess some level of control over the search conducted of the group's student members to prevent “unsuitable” or “inappropriate” material from entering the classroom environment.
Items at Digg.com can be sorted by end-user's criteria. Information about who “digg”s an item can be shared to create a hybrid implicit/explicit model, and end-user can share items found in search with friends more easily. Social bookmarking sites still use the whole community's actions as a means of displaying results to the end-user. While nodal relationships can be made and maintained, self-identified groups are not able to share, collaborate, or inform extensively as a group within the search environment itself. “Cliques” cannot be formed within such communities. Although “cliques” tend to imply a negative type of relationship, they are quite useful in regards to collective intelligence. A “clique” has established and/or common experience and interpretations that may allow the end-user to processes information better as well as faster. An example would be a group of medical doctors within a specialty versus patients with a particular medical condition. Although both groups might be interested in similar topics, some information is less valuable to one group perhaps due to ability to comprehend complex medical terms and concepts. Conversely, there might be some information that is usually only spread within the medical community that patients might find useful.
Furthermore, even among limited “communities” that have shared commonality, such as those that have similar political interests, hobbies, or backgrounds, the greater commonality of the aggregate as a whole is given priority and results provided do not cater to the particular end-user. In other words, shared data applies to all equally as a whole and results given to a particular end-user do not account for the priority of relationships of said end-user at any given time. Or, worded differently, these systems produce same results to a query based on the aggregate data of the whole and do not take into account varying social contexts end-users may find more useful at any given time. Current search systems are meant to be informative but they are not necessarily material to the overall end-user experience. For example, an end-user may receive items based on an overall population of users with a shared political interest on a topic and similar party affiliation. However, particular items of interest may differ further by age of end-user. The older generation may value an item lower than the younger generation who values the item very highly. Currently, such distinctions are not made nor presented.
Currently, the end-user has limited control over the social context in which searches are conducted prior to the initiation of each query or in the process of a query session in the case of persistent queries. Social context (implied or explicit) is assumed prior to each query. Any user interaction currently affects search systems as a whole and do not affect only the end-user or sub-set of end-users who share a similar goal.
Most search systems cannot accommodate group-based search where the queries are generated for the purpose of providing information for group use. A group consists of two or more end-users who want to collaborate, share, and/or inform during the search process and who want to combine search effort, such as those in the same or similar fields of research, membership affiliation, enterprise endeavor, or more. For example, an organization may have two or more purchasing agents buying supplies from vendors. Another example is a professor and graduate students collaborating on a common paper for publication. Current systems and methods for group-informing searches have been limited in scope.
“Explicit” social search is not the same as “group-based” search. While many explicit social searches may help inform the end-user's personal search, explicit search does not necessarily facilitate the common goals of a group of known end-users. In other words, explicit social search is broader in scope than group-based search. While nodal relationships can be made and maintained in current explicit “social” search models, self-identified groups are not able to share, collaborate, or inform extensively as a group within the search environment itself. In other words, the priority in explicit systems is placed on the end-user, but does not allow extensive group-based awareness outside of the nodal relationships of the community as a whole. “Cliques” cannot be formed within search environments, which might be useful for groups aimed toward a common purpose, goal, or mission.
Collaboration in the group-based search model has most widely involved referring and/or sending other users items from a search via instant messaging or electronic mail, shared files from search clients, streaming results, and web-access portals that require end-users to be present at a specific time when the search is conducted. A collaboration search model is employed for RSS feeds by Newsgator of Denver, Colo., with their “Enterprise 2.0 Collaboration”. The method employs a hub system of disseminating information in which RSS feeds are fed to specific people who then sort through the items and pass along relevant items to other collaborating members. The efficiency of this system depends on the productivity of a few people. This method does not allow for many people to swarm around information during a search to maximize use of time and utilize more people's expertise to aid evaluation of items, especially in a working group environment.
Newsgator's system is an example of a “hierarchical” search system, in which some users have a different status to other end-users in the system, for example, which could be related to importance, relevance, credentials, familiarity, etc. In Newsgator's system, status is created through the centrality of one end-user as “information gatekeeper”. A hierarchy is forcefully placed due to the inherency of hierarchy in the search system, so that working groups whose collaborating efforts are meant to be “egalitarian” (same status) in nature are forced to identify an “information gateway person” within Newsgator's system. Another example of a hierarchical system would be Technorati's system on accessing html. Each end-user has access to only what the end-user has designated as “open” to the class of users, (i.e., family, friend, acquaintance, public). However, Technorati's hierarchical system only applies to the content provider's imposed hierarchical system and not the searchers'. This model neglects the status of the searcher within the group for which the search is intended. In the school example, content providers can rate appropriateness of material, and filters can block material rated inappropriate or permit access to material rated appropriate; however, these filters blanket the entire system. Teachers who might want to incorporate additional filters to aid her students on a particular project cannot isolate particular items for said search independent of the system as a whole. In other words, filters are not able to be tiered seamlessly within a search system based on the various hierarchical levels represented in the school. Filters, and more importantly, interactions on search results, cannot be tied to individuals within hierarchical groups with each end-user given a level of permissions to how they can interact with results from a query to influence the group as a whole.
While some current technological approaches try to recognize the end-user as part of “real” social networks, these systems tend to only focus on peer-to-peer relationships, content, or aggregated communities as a whole. Most current search systems are not group-based aware, including mimicking the structure of nodal relationships within those groups. In other words, these search systems do not take into account end-users wanting to be identified as members of a group, conducting searches for the purpose of the group, each having a certain status within that group.
“Artificial Intelligence” systems have also been employed in search systems as a means to reduce “noise”. “Artificial intelligence” in reference to search is defined herein as computer implemented systems and methods applied to make predictions, inferences, evaluations, and/or guides to what an end-user may deem as valuable based on data gathered on end-user or plurality of end-users. In other words, AI systems work without requiring the end-user to provide a query. Intelligence applied to search systems become “smarter” through, for example, analyzing keywords in typical queries made by the end-user, the actions the end-user makes through a client, and/or the content of items chosen by end-user. Aggregated data from a plurality of end-users can also be applied to better predict what results a particular end-user might prefer or not prefer. The more an end-user or plurality of end-users interact with the system and patterns can be captured or ascertained, the more those patterns can be replicated and applied and even specialized for particular end-users. Blinkx of San Francisco, Calif., utilizes intelligence systems to infer the search intent of a user through the context of what is displayed to the user rather than their evaluation of what is displayed. Blinkx technologies use “implicit” queries, in other words artificial intelligence, to derive what other results a user might want. Blinx “contextual search” can be applied to many types of media, especially video. Another technology for “PICO” search engine provides the ability for users to view these implicit or explicit search results in “Smart Folders”. “Smart Folders” is similar to RSS in that it receives results users subscribe to and is populated with results provided to a stream, “channels”. “Smart Folders” and “Channels” are methods of delivering results rather than a complete search system in themselves. Intelligence, individual users, and content providers supply results for these channels for the smart folder. One display can have several folders that contain results for different media, such as blogs, RSS, and video. However, multiple channels cannot be integrated by the end-user with additional query criteria. Channels permanently contain results provided to the channel, unless removed. The channel itself cannot facilitate multiple different types of queries within the channel to provide results outside the channel. In other words, channels are independent much like Aware Search client folders or Newsgator RSS streams for collaboration, except they are smarter and have multimedia capabilities. Blinkx's channel system is not seamlessly integrated to allow end-users to explicitly drive search intent further. In other words, end-users are not able to choose how actual results should be related outside of “explicit query” or even “implicit” query”. Items in “smart folders” are not necessarily what end-users asked for or want, but are generally related. Further, they do not incorporate explicit collective intelligence, which allows end-users to share data through a more effective search collaboration method. Rather, end-users are only able to share a result with another end-user through common means mentioned above.
Current search systems over rely on intelligence to drive search. Overuse of artificial intelligence takes away the freedom of end-users to conduct search for their own unique purposes. While the good intention of intelligence is to streamline search for the end-user by narrowing results that might better match intention, these systems fail to allow end-users to input more evaluations on search intent outside of “queries” and artificial interpretations of search intent. Not to say intelligence algorithms are not useful, but the best method for understanding what an end-user (or group) wants is the brain of the actual end-user. By utilizing intelligence in systems applying “awareness” to actual results through explicit interactions of end-users based on particular results, not only are the interactions of an end-user applied to results for current queries, but the search system is also able to provide results to the end-user based on patterns of previous interactions by the end-user and/or a plurality of users.
There is a need and demand for more contextual intelligence. Many technologies, such as GPS, RFID, and bar code readers in cell phones, incorporated with search help provide users with content around the current context they find themselves. These query systems, however, have not been fully integrated with search to allow users to interact with results more fully. For example, a navigation system for an automobile might provide feedback based on location of the car, and users are able to interact fully in areas based on navigation. Some content is able to be provided based on query, such as the query “restaurants” will load information regarding restaurants located near the user. However, the end-user is not able to receive evaluations on particular restaurants based on other users without visiting a website possibly devoted to such. Even if such information could be retrieved, a small display would not be able to handle possibly thousands of comments. Ratings have generally been incorporated as simple ways to provide aggregated opinion. A search system that is actually aware of the user, knowing whose opinion he will value, would be more useful, especially since most people do not have the time to go through lots of information for simple decisions, such as “where to eat”. The data on results are currently not integrated in a way that can provide useful data based on social intelligence as well. Although these technologies are novel, such as barcode readers on phones, their uses are limited to quick, simple content, as more complex queries would take a long time to sort through information.
Current methods to bring “context” to search is through applying “local” search, such as Yahoo Local, from Yahoo.com of Sunnyvale, Calif. Yahoo Local uses many contextual features, such as maps overlaid with results, to provide users more information of what they might find useful. Although Yahool Local integrates many queries into one interface, i.e. map query, web page query, user's comments, these mash-ups to not provide integrated collective intelligence and contextual intelligence. The maps and comments for a query do not provide and are not integrated so as to reflect the opinions of only a sub-set of people whom the end-user prefers an opinion. Yahoo Local provides context, but the context is not socially aware or contextually aware of the end-user. A search for restaurant on Yahoo Local will provide a list of restaurants with corresponding information regarding restaurant, a map with numbers corresponding to restaurants results, and comments provided by users of the site on restaurants. The end-user is not able to narrow the scope further through a prompt so results reflect his preferences outside of the general query. For example, he might prefer the company of sports enthusiast, but is looking for a restaurant that might not necessarily be a “sports bar”. He might want a local pizza establishment with a “sports feel” or where sport enthusiasts go, but is not familiar with the area. Currently, these types of preferences to refine search cannot be captured through queries. Even if the user queried for “sports feel”, only restaurants that are directly characterized as such would fit matching keywords. Currently, there is no method to capture such context that can be used directly by the end-user so as to better present results he might prefer. While “local context” can be captured, narrower, integrated context (e.g. implicit cliques) are currently not being captured outside of “location” through queries and awareness in query results themselves.
Accordingly, there is a need for improved and more efficient search methods that facilitate intent-driven search that go beyond query sessions to provide users the ability structure connections among queries in regard to weight, significance, relevance, and/or other criteria to aid searches aimed at specific goals, projects, and/or relevance. Furthermore, there is a need to provide sessions in aware search systems to capture interactions on results to multiple, interrelated queries and to optimize data stores without destroying the integrity of the data searched. There is further need for improved and more efficient search methods that facilitate social awareness in search results to provide temporary or permanent associations of multiple end-users based on goals, relationship ties, or other nodal affiliations to structure results. There is a need to provide more flexibility in explicit search to model “real world” hierarchies when they are pertinent to the nature of the search. Similarly, there is a need for a system that allows a plurality of end-users to interact with results concurrently to achieve the best desired results for a group of end-users. There is a need for implicit search to provide end-users more control over inclusion or exclusion of social context and other context as applied to search. There is also a need to apply more contextual intelligence through linking other context to query results other than keywords. Accordingly, there is also a need for user and/or group directed search that provides greater interactivity with results on how results should be applied to queries.